In the last post, the fictional Toilet Toilers Interactive found themselves in a bit of a pickle with their GPU physics-and-everything-else pipeline. They might be regretting some of their technical choices, but it’s worth looking back at some of the things they might have considered during development. Why might they have decided to walk such an unusual path, and why didn’t bepuphysics v2?

GPUs are indeed fast

Bepuphysics v2 is primarily sensitive to two architectural details: memory bandwidth and floating point throughput. Moving from a quad core 4-wide SIMD CPU with dual channel DDR3 memory like the 3770K to a 7700K with AVX2 and higher clocked DDR4 can give you huge speedups. That’s despite the fact that it’s still just a quad core and the general purpose IPC/clock improvements from Ivy Bridge to Kaby Lake aren’t exactly groundbreaking.

Suppose we wanted to go crazy, and suppose I also managed to address some potential scheduling issues on high core count systems. AMD’s EPYC 7601 could be a good choice. Even though Zen 1 doesn’t have the same per-core floating point throughput as contemporary Intel CPUs or Zen 2, the massive number of cores enable a respectable 600+ gflops. With 8 memory channels of DDR4 memory, you can pretty easily hit 3 to 4 times more effective memory bandwidth than a 7700K (120+ GBps).

Now consider a high end graphics card. The 7601 is on the premium end of the pricing spectrum, so we’ll look at a 2080 TI: 13.4 tflops and 616 GBps memory bandwidth. The Radeon Instinct MI60 (with a large chunk of HBM2) has about 1 TBps bandwidth.

Even if you drop the budget into more reasonable territory, the gap between CPU and GPU persists. Graphics cards are simply designed for massive throughput and are ideal for highly parallel workloads that can make use of their extremely wide hardware.

Graphics are clearly a perfect fit for graphics hardware, but graphics cards have become progressively more general in their capabilities. We’ve moved from fixed function transforms up to a level of generality spanning cryptocurrency mining, protein folding, ray tracing, and yes, physics simulation.

So, if the Toilet Toilers thought they could get such a huge benefit by embracing GPU execution fully, why would I build bepuphysics v2 for the CPU?

Algorithms and architectures

Achieving decent multithreaded scaling generally involves phrasing the computation as a giant for loop where every iteration is independent of the others. Bonus points if you can guarantee that every iteration is doing the exact same type of work, just on different data. This applies to both CPUs and GPUs, but GPUs will tend to massively outperform CPUs in this use case.

Case study: collision batching

We don’t always have the luxury of doing the exact same type of independent task a million times. Consider the narrow phase of collision detection. In bepuphysics v2, each worker generates an unpredictable stream of collision pairs. The type, number and order of the pairs are not known ahead of time. This introduces some complexity. One potential GPU implementation would be to have a pass dedicated to extracting all the jobs from the collision pair stream and adding them to big contiguous lists. After the pass completed, you’d have the list of every sphere-sphere pair in one spot, every sphere-capsule in another spot, and so on. It could look something like this:

You could then dispatch the appropriate collision handler for each pair type. With a sufficient number of pairs, the GPU would achieve a decent level of occupancy.

But there remain some concerns. On the GPU, you’ve got potentially thousands of workers. Are you going to write a single shared set of type batches with global atomic counters? And you spent all that time writing everything in the first pass, only to then go back and read it all again in the second pass- that’s a lot of memory bandwidth used!

You’re not yet out of options, but now things start getting more complicated. Maybe you could create an intermediate set of type batches that accumulates a small local set in thread group shared memory that gets flushed to the main set only periodically, cutting down the number of global atomic calls. Maybe you do something clever with wave operations.

But even if the bookkeeping costs aren’t a concern, the theoretically pointless write-then-read issue is still there. Maybe you could take advantage of some features that are implied by hardware accelerated ray tracing. Ray tracing with unpredictable target surface shaders is actually a very similar problem, and as hardware adapts to support ‘callable shaders’ efficiently, options could open.

In CPU-land, we can just stick each pair into a thread local type batch. When the type batch count reaches some execution threshold- say, 16 or 32 entries- flush it. In most cases, that means the data in the type batch is still in L1 cache, and failing that, it will almost always still be in L2 cache. Swapping tasks from pair collection to executing the pair processor for a type batch does carry some overhead, but for a CPU, we’re talking about a few dozen nanoseconds on the high end. That doesn’t really matter when the execution of the type batch is expected to take a handful of microseconds.

As the workarounds showed, implementing something similar on a GPU is not fundamentally impossible, but it’s not natural for the device and you probably won’t be achieving that 13.4 teraflops number with a bunch of code like this (at least for a while). At a high level, CPUs are latency optimized and handle these sorts of small scale decisions easily, while GPUs are throughput optimized. That focus is reflected across the hardware, tooling, and programming model; going against the grain is uncomfortable.

Case study: dynamic hierarchy broad phase collision testing

Algorithms vary in their parallelizability. Some are stubbornly resistant to any kind of multithreading, others require a bit of effort to tease out the parallelism, and others are just embarassingly parallel. In many cases, when there exist sequential and parallel algorithms for the same task, the more sequential version has a significant advantage that can only be overcome by throwing quite a bit of hardware at the parallel version.

Bepuphysics v2’s broad phase contains an interesting example of this phenomenon. It uses a dynamically updated binary tree. To find out which collision pairs exist, all leaves of this tree must somehow be tested against the tree.

The naive algorithm is very simple and is a decent fit for a GPU or other very wide machine: loop over every collidable and test its bounding box against the tree. Every test is completely independent.

But note that we’re loading the tree’s nodes over and over again. The whole tree could be multiple megabytes, so tiny core-local caches won’t be able to serve most requests. Even without the extra cost of hitting more distant caches or main memory, that’s still a lot of bounding box tests. As always, there are some potential optimizations or workarounds, and as usual, they come with some complexity penalties.

There exists a very simple algorithm that avoids the need for most of this work. Consider two separate bounding trees- test the roots against each other; if they intersect, test all combinations of their children (for pair of binary trees, childA0-childA1, childA0-childB1, childB0-childA1, childB0-childB1); for the pairs that intersect, recursively continue the same process. The result is all overlaps between leaves in tree A and tree B.

Now, we can go one step further: run that same process, except between a tree and itself. Note that there’s no need to do many of the intersection tests because any node obviously overlaps with itself. Not only has the number of intersection tests dropped considerably compared to the naive approach, but we’ve also eliminated all the redundant node loads.

This algorithm is not horribly sequential- it spawns independent work recursively- but it’s not embarassingly parallel either. In other words, if you’ve got 64 fat cores, you can create enough worker tasks to fill the hardware cheaply. But, if your target architecture has many thousands of very weak threads, suddenly the setup work is significant.

(It’s worth mentioning that ‘threads’ exposed by graphics APIs aren’t mapping to the same sort of thread as you get on an x86 CPU. They’re more like lanes in wide SIMD units. You can take advantage of this sometimes, but again, going against the grain comes with a complexity penalty.)

So what does all this mean? To naturally fit a GPU, you’ll generally want to pick algorithms that suit its execution style. Sometimes this means paying an algorithmic penalty to use a more parallel implementation. In these cases, you sacrifice some of that crazy compute power in order to use it at all.

Of course, even a handicapped GPU can still beat a CPU sometimes.

Case study: bookkeeping

The least exciting parts of a simulation can sometimes be the hardest to bring over to extremely wide hardware. Consider the process of adding constraints to the solver based on collision detection results. Every constraint addition requires multiple sequential steps and separating them into even somewhat parallel tasks involves terrible complexity.

Each new constraint must identify a solver batch that does not share any bodies with the new constraint, then it must find (and possibly create or resize!) a type batch for the new constraint to belong in. And then it has to reach into the per-body constraint lists to ensure a traversable constraint graph. Andsoon. It’s tied for my least favorite part of the engine, and it’s all because I attempted to extract parallelism where there was very little available.

And then you have even more fun things like sleeping and waking. Beyond merely adding a bunch of constraints and updating all the related bodies to match, it also moves a bunch of bodies and updates the broad phase at the same time. (I’d link a bunch of stuff related to it, but I can’t be bothered to go find all the tendrils.)

Even with all that effort, the scaling is still pretty bad. Especially sleep/wake which bottlenecks badly on the broad phase modifications. I aim to improve that at some point, but I really, really don’t look forward to it. On the upside, our big fat latency optimized CPUs still do reasonably well. Bookkeeping is rarely a noticeable fraction of frame time unless you happen to be sleeping a 16000-body pile.

Without the benefit of big x86 cores to brute force through these locally sequential chunks, this work could become a serious problem. Trying to do all of this work on a few GPU ‘threads’ (in the HLSL sense of the word) is a nonstarter. Realistically, it would require a completely different approach to storage- or punting the work to fatter core. It wouldn’t be enjoyable in any case.

The pain of asynchrony

Any time your simulation is executing out of step with some interacting system (like game logic), a blob of complexity is introduced. All interactions between the systems must be carefully managed. This applies to asynchronous use on both the CPU and GPU, but running a simulation on the GPU will tend to imply a thicker barrier. Even merely moving data across PCIe takes extra time.

Game logic often benefits from fairly tight integration with the simulation. Writing a callback that can modify the simulation state or report data on the fly gets a lot harder if that callback needs to execute on the GPU. In practice, a whole lot of simulation state will only be indirectly accessible. You can definitely still work with it, but everything gets a little bit harder.

There’s also the option of embracing it entirely like the Toilet Toilers did- execute even most of game logic on the GPUs. I might call you nuts, but you could do that. The helpfulness, speed, and stability of tools in GPU land typically struggle to meet the expectations you might have from working in CPU land…

All the asynchronous pain disappears if the only consumer of your simulation is graphics and your graphics and simulation are synchronized. Noninteractive debris, many forms of cloth, particles and other decorative simulations are great use cases for GPU physics that avoid asynchrony’s suffering entirely. That just wasn’t my target use case for bepuphysics v2.

Comparative advantage

In many games, all the crazy horsepower that a GPU brings to the table is already spoken for. Rendering 4K@120hz with ray traced global illumination is still beyond all current hardware, and by the time we have the hardware for it, we’ll be trying to render dense lightfields for VR or something.

Is it worth turning down some of that graphical fanciness to run physics? If we assume that a GPU implementation is 4 times faster and that all the concerns above are dealt with, we could move a 6 millisecond CPU simulation to the GPU for a mere 1.5 milliseconds. If the simulation and rendering are both targeting 144hz, then you’ve cut your rendering budget by 20%, but massively increased your CPU budget! … now what are you going to do with that CPU budget?

You could waste a bunch more time in the driver by spamming draw calls, I suppose. You could generate a wonderful number of draw calls with DX12 or Vulkan and multiple threads. Or you could use indirect rendering and a gpu driven pipeline…

Maybe you could do some fancy sound tracing and dynamic audio effects! But a lot of the queries would fit the simulation structures really well, and pulling that across PCIe every frame seems wasteful, and the actual math involved would fit the GPU…

Or maybe use some fancy machine learning techniques to animate your characters, but then GPUs (especially ones with dedicated hardware for it) do inference way faster than CPUs…

I don’t mean to imply that there’s nothing you could spend that CPU time on, just that a lot of heavy lifting does map well to the GPU. Despite that, there’s no point in leaving the CPU idle. It makes sense to give the CPU work that it can do reasonably well, even if the GPU could technically do it faster- because the GPU is doing something else already!

(Backend use cases can be interesting- the lack of rendering on the server could open up space for many more jobs. You’d still suffer from many of the other issues listed here, but at least you’d have the computational resources freed up.)

The deciding factor

I’ve gone through a variety of concerns with GPU simulations, but I’ve also tried to offer some workarounds in each case. It’s definitely possible to make a speedy GPU simulation- it has been done, after all.

But there’s one problem that I can’t find a way around. Making a complex GPU-heavy application run reliably across different hardware vendors, hardware generations, operating systems, and driver versions is extremely painful. Past a certain level of complexity, I’m not actually convinced that it’s possible without having a direct line to all the involved engineering teams and a few metric hecktons of dollars. I do not have that many dollars, and not many people know I exist; hitting a particularly dangerous driver bug can kill an entire line of research.

And, even if I manage to achieve a reasonable level of stability somehow, regressions in newer drivers- especially on older hardware- are not uncommon. The fate of the Toilet Toilers was only slightly exaggerated, and only in terms of temporal density.

I can try to report all the issues to all the different parties involved, but honestly, I can’t blame them if they don’t fix a problem even 5 years later. Some of these problems are just weird little corner cases. Some of them don’t apply to any modern hardware. It makes perfect sense to spend limited development resources on the projects that are actually going to drive many millions or billions of dollars in sales. (Maybe one day!)

You might notice that this line of thinking almost suggests that any GPU development is a lost cause for a small player, not just GPU physics. As frustrating as the ecosystem is, I’m not willing to go that far. There are a few important distinctions between graphics and physics.

In a physics simulation, the result of one time step feeds into the next, and into the next, and so on. If any stage fails at any point, it can cause the entire simulation to fail. For example, a single NaN will propagate through a constraint graph within a frame or two. Within another frame or two, the simulation will probably crash.

Graphics tend to be slightly more resilient. There are still types of bugs which can bring everything crashing down, certainly, but most of the milder bugs can be survived. They might just result in a bit of visual corruption here or there- once it’s out of any temporal accumulation windows, the problem may resolve.

In other words, in graphics-like tasks, data dependency chains tend to be short. Error does not have a chance to feed upon itself catastrophically, or the error is bounded.

Further, graphics engines often have lots of tuning knobs or multiple implementations of effects for different quality levels. Bugs can sometimes be worked around by fiddling with these knobs. Physics simulators usually don’t have as many degrees of freedom.

Finally, if you are working on graphics as a smaller developer, chances are you’re targeting just one API and operating system. Focusing on, say, DX12 on Win10 alone won’t be easy, but it’s not the nightmare that supporting other operating systems/APIs simultaneously would be. (The same can be said for some client-only GPU physics use cases, but if you want to run physics serverside, it’ll probably need to run on things that aren’t windows- because the server licensing model is the only thing worse than driver bugs.)

But even for graphics, the situation is nasty enough that using one of the super popular graphics engines is wise if you intend to target a variety of platforms. It’s a well-worn path that a thousand other developers have walked already; they have suffered for you.

Conclusion

If I built bepuphysics v2 to run on the GPU, I could probably make it faster. It would take a long time and I’d want to gouge out my eyes at many points during the process, but I could do it.

It wouldn’t match up with how I want to integrate it into other projects, and I have other things I can spend those GPU cycles on.

August 23, 2019, 2:23 PM

You are one of the four founding members of Toilet Toilers Interactive, a development studio riding a wave of popularity after a successful paid alpha launch.

Punching and Whatnot, your genre-fusing open world exploration game about physically punching things, attracted an early and devoted audience on the promise of its marketed-as-revolutionary technology stack.

Development videos of many thousands of punchings simulated in real time spread virally through the latest and most confusing forms of social media.

Critics hail your game as 'much better than it sounds'. Development costs have largely been recouped.

Your team, a group of technological pioneers in the field of simulated pugnacity, have the attention of the gaming world.

And this is your job.

"It's really not that hard, you just gotta..." you say, carefully manipulating a plate covered in crumbs.

"You're using your thumb," says your cofounder Minji, leaning back in a chair across from you with her arms crossed in skepticism.

"No I'm not," you say, putting the plate back down on the desk.

"Yes you were, I saw it."

"It was touching, but the thumb was barely even acting as a stabilizer."

"A person without thumbs doesn't get to use their thumbs as barely-stabilizers. Do it again, no thumb."

"Fine," you huff, making a show of extending your thumb as far as possible before grabbing the plate between your forefingers. Your phone vibrates, but this task demands your whole focus.

"See, it's easy," you begin, but are interrupted by the sound of rapid British-sounding footsteps approaching the doorway.

"ALL THE SERVERS ARE DOWN!"

August 23, 2019, 3:13 PM

You are all quite proud of the infrastructure you've built. Almost everything that could be automated, is. Need to update the servers? Zero downtime rolling automated installs. Crashes or hardware failure? Automatic load balancing fills in the gap as needed. Want to check server status? Fancy graphs on your phone.

All without using any off the shelf technologies. Everything from scratch. (It added 15 months to the project, which, as you frequently tell yourself, isn't really that long and it was definitely probably worth it.)

And, as a testament to just how robust it all is, it took three whole weeks after release to hit your first catastrophic failure and multi-hour outage.

"The servers are up on the previous deployment. Backups were untouched, all data retained," Minji says from across the conference table.

You smile smugly, nodding to yourself. The backup system had been your baby. To your right, Geir (whose name is pronounced something like Guy, but who asserts that you are still saying it wrong, but that's his fault for having such a British name) notices your self-satisfaction and gives you a look which you take as affirmation of your work's quality.

"Nominal levels of forum angst. I don't think we'll have to do more than offer a brief explanation," adds Kazimiera from your left, who had settled into a sort-of-community-manager role in addition to the rest of her regular developmental responsibilities.

"So, unless someone else snuck something into that last image, the only change was a new graphics driver. There's a new WHQL release that apparently fixed some of the tiled resource slowdowns we had to work around. I put it on the test servers and it came back all green," Geir said.

"So, it's possible that we may have forgotten to address a small issue wherein the tests can sometimes, under complex conditions involving driver freezes, time out and return success, instead of not success," you explain to Geir, who had missed the fun. He squints at you.

"How does the tech support forum look?" asks Minji.

Kazimiera clicks around.

"A couple of 'server down' posts... Oh, someone is actually playing on integrated, and it's working! Except all skinned characters have little flickering black dots at every vertex."

"As it should be," says Minji, nodding.

"And about four 'help my game is completely broken' in the last few days, which is two or three more than usual."

"It went WHQL a few days ago," offers Geir.

Everyone sits quietly for a moment, staring into space.

"Alright," starts Minji, slapping the table. "It looks like we need some proactive action, I'm gonna be the scrummer. The scrum lord. It's time to get agile."

"But are you a certified scrum lord?" you ask.

"Kazi, kindly draw us a burndown chart," she says, ignoring you.

Kazimiera gets up and walks toward the whiteboard.

"British graphics expert," she says, pointing at the sighing Geir. "The surface space allocator uses sparse textures right? Would you confirm that everything is broken on the client?"

Geir nods.

Kazimiera is finishing up the labels on her chart. A line starts in the upper left and heads to the lower right, carefully dodging an older drawing of a cat. The line spikes upward to a point above the starting height a few times. The horizontal axis is labelled TIME, and the vertical axis CATASTROPHIC BUGS.

"Thank you, Kazi. I feel adequately scrummed. I'd like your help actually-fixing the test harness. And we should probably post some warnings."

Kazimiera caps her marker and returns to her seat.

Minji turns to you.

"There aren't a lot of GPU jobs on the backend, and you happen to have written all of them," she says.

You make a small noise of acknowledgement tinged by indignity.

August 23, 2019, 5:56 PM

The backend physics was, in fact, not working on the new driver version. Your workstation's card, a slightly older generation with an architecture named after a different dead scientist, exhibits the same problem. The reference device works fine.

With some effort and bisection, you've gotten to the point where things do not instantly crash. On the downside, the only active stage remaining is a single piece of the broad phase pair flush. And, given dummy input, it's producing nonsense results.

You launch the program again with a VS capture running, and it immediately crashes.

Pursing your lips, you start up NTRIGUE, a vendor created tool that was recently rebranded for unclear reasons. It looks like it's working, but starting a frame capture immediately crashes.

You take a deep breath and exhale slowly.

August 26, 2019, 1:52 PM

"Lots of things are busted. For example, with anisotropic filtering x8 or higher enabled, while indexing descriptor arrays in the pixel shader, in the presence of sufficiently steep slopes at over one hundred kilometers from the camera, the device hangs. I am not yet sure how to narrow down the repro further. Also I'm not sure if tiled resources should be considered to exist anymore."

Kazimiera clicks a few more times. "Only one dispatch is actually completing without hanging the device," she explains. "Your commit descriptions are admirably thorough. Except this one of an ascii butt."

"On the upside, we should have the test suite fixed today or tomorrow," says Minji.

"Content pipeline's fine, by the way. Auto-animation net is still working flawlessly," adds Kazimiera.

"The Demon Prince of Drivers is patient," you warn.

August 27, 2019, 2:40 PM

You slump into your chair. Geir, arriving sooner, has already slid far enough down that only his shoulders and head are visible above the conference table.

"Praise be to scrum," chants Minji.

"My turn to start," you say. "FFFfffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff," continuing until you run out of breath.

"Do you think they would fix it if I just submit a bug report that is the sound of frustrated yelling?" you ask, sliding down your chair a little.

"That may be a significant fraction of the total reports, you should put in some more effort so it will get noticed. Maybe try yelling louder," offers Kazimiera.

"That's a promising idea," you say, rubbing your chin. "Alright, well, under some conditions I have not yet narrowed down, UAV barriers seem to do nothing, and the physics is a series of compute dispatches, so, hooray. For clients, I may be able to work around it by forcing some sort of useless transition or other stall. But it will be a pretty big performance hit and there's not enough headroom for it to be used on the backend, so we can't update the drivers there."

"Interesting. Four more agile points for you. As for me, I've been tracking down some of the non-crashy problems on the client. It looks like all indirect executions now cost a minimum of about max command count times one or two microseconds, regardless of the actual count in the count buffer. I should be able to have that reported today. For this, I earn many points," says Minji.

"And I've just finished a little optimization on the proxy server that cuts minimum latency by half a millisecond," says Kazimiera.

"How does it feel, up in your ivory tower? Frolicking lightly above the clouds, free to make forward progress?" you ask.

Kazimiera smugs.

August 28, 2019, 4:33 PM

"So, I worked around the missing barriers at a slowdown of about four hundred percent," you explain. "To mitigate this slowdown, I propose spending three hundred and fifty thousand dollars on new hardware instead of continuing to work on it because I do not want to work on it."

"Any response from the vendor?" asks Minji.

Kazimiera hits F5. "No."

"Cool, cool, cool, cool, cool, cool, cool, cool, cool," says Minji, tapping a pen against the conference table to match. She sinks sideways, propping herself up with one elbow. "Lord British," she says, pointing the pen at Geir without turning, "tell me what you got."

"Sometimes I wonder if it was a good idea to leave Norway."

"Oh, my aunt went there once! Pretty long drive out, I heard. That's like a hundred miles northeast of London, isn't it?" asks Minji earnestly.

Geir shook his head, badly suppressing a smile.

"You know, we're semifamous now," starts Geir. "And a whole lot of their customers are immersed in our completely broken mess. They will probably be a little more motivated to work with-"

"Um," interrupts Kazimiera.

Everyone looks over.

"Windows 10 Creative Pool Summer Hangout update is rolling out. Staged, but should expect a lot of users to be on it within a week or two. It apparently changes some bits of the graphics stack."

If you'd like to try a brain teaser, imagine you’ve got subtract, multiply, and lane shuffle operations that can handle 4 lanes of data at the same time. Try to come up with the minimal number of operations needed to express the same mathematical result for a single pair of input 3d vectors.

Then, compare it to the scalar version. Without spoiling much, it's not going to be 4x faster with 4-wide operations.

Problems like this show up any time you try to narrowly vectorize scalar-ish code. You rarely get the benefit of 128 bit operations, let alone newer 256 bit or 512 bit operations. There just aren't enough independent lanes of computation to saturate the machine with real work.

Fortunately, most compute intensive programs involve doing one thing many times to a bunch of instances. If you build your application from the ground up assuming this batch processing model, it doesn’t even involve more difficulty than traditional approaches (though it does havedifferent difficulties).

They’re both very similar at the level of memory accesses. Load the relevant whole struct instance from the array (hence array of structures!), do some work with it, store it out. At a lower level, the SSE version is a bit weirder. For example, the Vector3AOS.Cross3ShuffleSSE function is not quite as intuitive as the earlier cross product code:

Despite its ugliness, the SSE variant is faster even with the current alpha state of intrinsics support by about 25-30%. Still a far cry from 4x faster.

To do better than this, we need to make use of the fact that we’re doing the same thing a million times in a row.

Structure of Arrays

Array of structures (AOS) lays out memory instance by instance. Each struct contains all the data associated with one instance. That’s pretty intuitive and convenient, but as we’ve seen, it’s not a natural representation for wide vectorization. At best, you’d have to convert the AOS memory layout into a vectorized form at runtime, perhaps walking through multiple instances and creating vectorized bundles of instance properties. But that takes valuable time that we could be spending on actual computation!

(Note that the above implementation is uglier than it needs to be- the goal was to keep it conceptually separated from the next approach, but a real implementation could be less verbose.)

This turns out to be about 2.1x faster than the original scalar version. That’s an improvement, even if not quite as large as we’d like. Memory bandwidth is starting to block some cycles but it’s not the only issue.

One potential concern when using SOA layouts is an excessive number of prefetcher address streams. Keeping track of a bunch of different memory locations can hit bottlenecks in several parts of the memory system even if each access stream is fully sequential. In the above, the 13 active address streams are enough to be noticeable; if expressed in the same kind of SOA layout, the current Hinge constraint would have 47 streams for the projection data alone.

You could also try changing the loop to instead perform each individual operation across all lanes before moving on to the next one (i.e. multiply all ay * bz, store it in a temporary, then do all az * by and store it, and so on). That would indeed eliminate address streams as a concern given that there would only ever be two or three in flight at a time. Unfortunately, every single operation would evict the L1, L2, and with a million instances on many consumer processors, even all of L3. No data reuse is possible and the end result is a massive memory bandwidth bottleneck and about 3.5x worse performance than the naive scalar version. Now consider what it might look like if the loop were modified to process a cache friendly block of properties at a time…

Tying it together: Array Of Structures Of Arrays

We want something that:

allows efficient vectorized execution,

is usable without SPMD/SIMT tooling (see later section),

feels fairly natural to use (preserving scalar-ish logic),

is cache friendly without requiring macro-level tiling logic, and

doesn't abuse the prefetcher.

There is an option that meets all these requirements: AOSOA, Array Of Structures Of Arrays. Take the bundled Structure Of Arrays layout from earlier, restrict the array lengths to be the size of a single SIMD bundle, shove all of those smaller bundle-arrays into a struct, and store all those bundle-array containing structs in an array. Or, to put it in a less grammatically torturous way:

Performance wise, this runs about 2.4 times as fast as the scalar version. Not 4x, but as you might guess by now that is partially caused by a memory bandwidth bottleneck. The benchmark simply doesn’t have enough math per loaded byte. (If you change the code to work in L1 only, it bumps up to 3.24x faster than the scalar version. Still not perfect scaling for the 4 wide operations used on my machine, but closer- and the gap can close with additional compiler improvements.)

AOSOA layouts are used in almost every computationally expensive part of the engine. All constraints and almost all convex collision detection routines use it. In most simulations, these widely vectorized codepaths make up the majority of execution time. That’s a big part of why v2 is so speedy.

On autovectorization and friends

“Sufficiently smart compilers” could, in theory, take the first naive scalar implementation and transform it directly into a fully vectorized form. They could even handle the annoying case where your data doesn’t line up with the SIMD width and a fallback implementation for the remainder would be better (which I’ve conveniently ignored in this post). With no programmer effort whatsoever, you could take advantage of a machine’s full capacity! How wonderful!

Unfortunately, the CoreCLR JIT does not support autovectorization at the moment, and even offline optimizing compilers struggle. C, C++, C# and all their friends are extremely permissive languages that simply don’t provide enough guarantees for the compiler to safely transform code in the extreme ways that vectorization sometimes demands.

In practice, the promising magic of autovectorization feels more like coaxing a dog across a tile floor. “Trust me, it’s okay girl, c’mere,” you say, “I know it’s sometimes slippery, but I promise you’re really going to be okay, I made sure you won’t hit anything, look, I’ve got your favorite tasty PragmaSimd™ treat”- and then she takes a step forward! And then she yelps and steps back onto the carpet and you go back to reading your dog’s optimization manual.

But autovectorization and intrinsics aren’t the only way to handle things. By restricting language freedom a little bit to give the compiler additional guarantees, you can write simple scalar-looking code that gets transformed into (mostly) efficient vectorized code. The most common examples of this are GPU shading languages like HLSL. Each ‘thread’ of execution on modern GPUs behaves a lot like a single lane of CPU vector instructions (though GPUs tend to use wider execution units and different latency hiding strategies, among other nuances).

This approach is usually called SPMD (Single Program Multiple Data) or SIMT (Single Instruction Multiple Thread) and it can achieve performance very close to hand written intrinsics with far less work. It’s less common in CPU land, but there are still options like ISPC and OpenCL. (Unity’s Burst may qualify, but it’s still in development and I’m unclear on where exactly it sits on the spectrum of autovectorizer to full-blown SPMD transform.)

Further, even in HLSL, you are still responsible for an efficient memory layout. Randomly accessing a bunch of properties scattered all over the place will obliterate performance even if all the code is optimally vectorized.

So there’s no fully general magic solution for all C# projects at the moment. If we want to maximize performance, we have to do it the hard(er) way.

One size doesn’t fit all

Clearly, not everything needs to be vectorized, and not everything needs to use an AOSOA layout. Even when performance is important, AOSOA is not automatically the right choice. Always defer to memory access patterns.

One example of this in bepuphysics v2 is body data. All of it is stored in regular old AOS format even though the PoseIntegrator does some nontrivial math with it. Why? Because the PoseIntegrator isn’t a large chunk of frame time, it’s often already memory bandwidth bound, and body data is frequently requested by collision detection and constraint solving routines. Storing all of a body’s velocities in a single cache line minimizes the number of cache lines that the solver has to touch.

While bepuphysics v2 doesn’t use pure SOA layouts, they can be valuable. If you don’t have excessive numbers of properties and there are many accesses to individual properties without their neighbors, it’s a great fit. Indexing with SOA is a little easier than in AOSOA too.

Addendum: performance

This chart shows all the different approaches and their performance on my 3770K, plus some variants that I didn’t discuss earlier in this post.

AOS Numerics uses the System.Numerics.Vector3 type but ends up being fractionally slower than the fully scalar path. That’s not surprising- dot products and cross products don’t have a fast path. In fact, Vector3’s cross product is implemented in a completely scalar way.

SOA Doofy is the ‘compute a single operation at a time and evict entire cache’ implementation noted in the SOA section. Not ideal.

AOSOA Intrinsics Unsafe and AOSOA Intrinsics Load/Store are a couple of investigations into codegen on the new platform intrinsics types under development. They’re actually faster when AVX is disabled in favor of SSE alone, but even then they’re still slower than the System.Numerics.Vector AOSOA implementation. Both suffer significantly from L1/store bottlenecks caused by aggressively shoving things into memory rather than just holding them in registers. (Not too surprising in the Load/Store variant where I explicitly told it to do that.)

I wouldn’t put too much stock in the intrinsics numbers at this point- I ran this on a daily build of the alpha, after all. There’s probably a way to tease out some better codegen than represented above, and if not, the situation will likely improve with further work in the compiler.

Suppose you have a function that does some complex logic surrounding some other chunk of user-supplied logic. For example, a function that takes a list of bodies and constraints and applies the constraints to the bodies, like so:

The loop grabs the body references and hands them to the relevant constraint. Nothing too unusual here; it works, it’s fairly simple.

And it’s way slower than it needs to be.

Virtual obstacles

The constraints span contains elements of type IConstraint. Even though we only have one IConstraint implementation in the above, imagine if this was a library- there could be any number of other IConstraint implementations, and the constraints span might contain a bunch of different types. The compiler punts responsibility for calling the proper implementation to execution time.

That punt results in virtual calls. From the ApplyConstraintsThroughInterface loop body, the IL (on the left) shows this as ‘callvirt’, and without any other help, it pretty much guarantees some heavy uninlineable function calls in the final assembly (on the right).

Note that not every interface or abstract function call will result in a full virtual call in the final assembly. The JIT can perform devirtualization sometimes, but I built this example to prevent it.

Boxing

We can store any IConstraint-implementing reference type instance in the span because it just stores the references. But value type instances aren’t directly tracked by the GC and don’t have a reference by default. And, since we’re talking about a contiguous array with fixed stride, we can’t just shove a value type instance of arbitrary size in.

To make everything work, the value type instance gets ‘boxed’ into a reference type instance. The reference to that new instance is then stored in the array. If you go look at the IL generated when sticking the IneffabilityConstraints into the IConstraint array, you’ll see this:

L_00cf: box CodegenTests.GenericsAbuse/IneffabilityConstraint

In other words, sticking a value type into a slot shaped like a reference type automatically creates a reference type instance for you. It works, but now you’ve given the poor GC more work to do, and that’s rude.

Sure, you could stick only to reference types, but then you’re on the path to having a really complicated web of references for the GC to track. Frankly, that’s pretty inconsiderate too.

Specializing for speediness

Ideally, we could eliminate virtual calls and any other form of last-second indirection, allow inlining, and avoid any boxing.

This is possible if we’re willing to organize the incoming data into batches of the same type. This opens the door, at least conceptually, for specializing the entire loop for the specific type we’re working with. How would this look?

Almost identical! Rather than having a span over any possible IConstraint, this instead takes a span of a specific type TConstraint that implements IConstraint. If we peek at the IL and assembly of the loop body again:

The IL is pretty similar. Roslyn is still outputting callvirts, except now they are ‘constrained’. This makes all the difference. Thanks to the extra type awareness, the JIT can not only eliminate the virtual call but also inline the contained logic.

That alone accounts for about a factor of 4 speedup in a microbenchmark comparing the two approaches. A larger program with more logic per virtual call would not show quite as much difference, but the cost is real.

But wait, hold on… What’s this? If we do nothing more than change the IneffabilityConstraint to a class instead of a struct, the assembly becomes:

The calls have returned! It’s still about 30-50% faster than the original pure interface version, but nothing is inlined anymore. The JIT should still have all the necessary type knowledge to inline… right?

Sort of. The JIT handles value types and reference types differently when it comes to generics. The JIT takes advantage of the fact that all reference types are able to share the same implementation, even if it means specific implementations can’t be inlined. (This behavior is subject to change; for all I know there might already be cases where certain reference types are specialized.)

But even if the JIT wanted to, it can’t efficiently share implementations across value types. They’re different sizes and, unless boxed, have no method table for indirection. So, rather than boxing every single value type to share the same code, it chooses to output a type-specialized implementation.

This is really useful.

In BEPUphysics v2

This compiler behavior is (ab)used all over the place in the engine. You can stretch this from simple things like efficient callbacks all the way over to almost-template-hell.

One of the first places users might run into this with BEPUphysics v2 is in narrow phase callbacks, specified when the Simulation.Create function is called:

These are directly inlineable callbacks from the narrow phase’s execution context. Collision pairs can be filtered arbitrarily, contact manifolds can be accessed or modified before they’re used to create constraints, materials can be defined arbitrarily. There are no assumptions about collision filtering or material blending built into the engine, and there are no collision events in the library itself. Events could, however, easily be built on top of these callbacks.

Another case: want to enumerate the set of bodies connected to a given body through constraints? No problem. In the Simulation.Bodies collection:

Where IForEach<T> allows the user to provide a callback that will be executed for each connected body:

public interface IForEach<T>
{
void LoopBody(T i);
}

The struct-implementing-enumerator-interface pattern is used quite a few times. The ReferenceCollector, in particular, sees a lot of use. It provides the simplest ‘store a list of results’ enumerator.

There are plenty more examples like the above, particularly around ray/query filtering and processing. Can we go a bit further than callbacks? Sure! Here’s a real-world expansion of the above toy ‘constraints’ example:

Without copying too much spam into this blog post, this generic definition provides enough information to create a number of functions shared by all two body constraints. The SolveIteration function looks like this:

The TypeBatch, containing raw untyped buffers, is given meaning when handed to the TypeProcessor that knows how to interpret it. The individual type functions don’t have to worry about reinterpreting untyped memory or gathering velocities; that’s all handled by the shared implementation with no virtual call overhead.

Can we go even further? Sure! Let’s look at the ConvexCollisionTask which handles batches of collision pairs in a vectorized way. As you might expect by now, it has some hefty generics:

To paraphrase, this is requiring that the inputs be two vectorizable shapes and a function capable of handling those shape types. But the actual batch execution does some more interesting things than merely inlining a user supplied function. Check out the loop body:

Typically, you really don’t want to wait until the last second to perform a branch. So what’s up with this?

The TPairWide.OrientationCount and HasFlipMask properties all return constant values. Since the JIT is already creating a dedicated code path for the specified type parameters (they’re all value types, after all), it takes into account the compile time known value and prunes out the unreachable code paths. The final assembly will only include whichever orientation count path is relevant, and the flip mask chunk won’t exist at all unless required. No branches involved!

The JIT can also recognize certain kinds of type condition as constant. In other words, when using value types with generics, C# supports something similar to C++ template specialization.

If this has awakened a dark hunger within you, you might also like the convex sweep generic definition which abstracts over different shape types as well as different shape path integrations.

And why not create collection types that vary not only over element type, but also over the internal buffer type, any required comparisons, and memory pool types? What could go wrong?

Summary

C# generics might not be a Turing complete metaprogramming language, but they can still do some pretty helpful things that go beyond just having a list of a particular type. With a bit of compiler awareness, you can express all sorts of simple or complex abstractions with zero overhead.

C# is a garbage collected language. Each time the ‘new’ keyword is used to create an instance of a reference type, the GC (garbage collector) begins tracking it. The programmer doesn’t have to do anything to free the memory; instead, at some point after the memory is guaranteed to no longer be in use, the GC takes care of it.

This tends to improve productivity by eliminating manual memory management as a concern. There’s no easy way to leak memory if you stick to GC tracked references and never use any unmanaged resources. Allocation also tends to be very cheap thanks to the GC’s bookkeeping.

But there is a dark side: you will pay the price for all those allocations when the GC runs. Worse, your control over when this process occurs is limited. Your game might drop a frame (or five) at an inopportune moment.

To minimize this issue, performance-focused C# applications have to be kind to the GC. Ideally, that means not making any garbage to collect, or failing that, making collection very cheap.

The most common form of GC kindness is to reuse references to instances rather than simply instantiating new ones on demand.

This has a couple of problems:

Pooling instances works against generational garbage collection. Gen0 collections are pretty cheap. Higher generations are more expensive, and allocations are only promoted to the higher generations if they stay alive. The entire point of pools is to keep allocations alive, so if those pooled instances are eventually collected, it will be more expensive.

The GC has to track all the living references somehow. Conceptually, this is a graph traversal where the nodes are the tracked allocations and edges are references between those allocations. The more nodes and edges there are in the graph, the more work the GC has to do. Pooling directly increases the size of this graph. Even if the pooled instances are never collected until the application is torn down, any other garbage collection will suffer from the increased heap complexity.

Using GC-tracked instance pools is, in other words, a commitment to never causing a GC at all, or being okay with a potentially long hitch when a GC does eventually occur.

That might be okay for an application in some cases where you have full control over memory allocation, but a library would ideally not assume anything about the user’s memory management strategy.

For the same reason, it would be unwise for a library to trigger a bunch of collections under the assumption of a simple heap.

We could try to salvage instance pooling by exerting control over the GC. More recent versions of .NET allow you to suppress collections during critical times. That can be handy- for example, in HFT, you would probably want to defer GC until after a trade is fully processed. In games, you might be able to defer many collections into the idle time between frames.

But the work is still being done. Those are CPU cycles you could use for something else.

What’s left?

If we want to eliminate the GC as a concern, we can just… not use the GC. Plenty of languages don’t even have a concept of a GC; this isn’t a new idea. How does this look in C#, a language which was built on the assumption of a quick GC?

Value types (like int, float, other primitives, and any struct) differ from reference types (any class) in that value types are not directly tracked by the GC. That is, whatever contains a value type instance is responsible for the memory of the instance. From the GC’s perspective, an array of N floats is a single tracked allocation, not N different allocations.

The only time a GC needs to concern itself with a value type instance is if the value type contains a GC-tracked reference. The GC can basically skip over “pure” value types that contain no references. These are sometimes called ‘unmanaged’ or ‘blittable’ types. (There’s some complexity here regarding generics, but that’s more of a language thing than a GC thing.)

The fact that the GC can ignore unmanaged value types means you can stick such types anywhere you want, including in unmanaged memory:

You are free to write basically-C in C# if you really want to. Hooray?

The BEPUutilities library used by BEPUphysics v2 implements its own memory management. It’s not built on AllocHGlobal at the moment- it instead allocates big chunks out of the Large Object Heap and then suballocates from them- but that’s a fairly minor implementation detail.

The BEPUutilities BufferPool class is the source of most memory in the library. It’s a simple power-of-2 bucket buffer allocator that provides fast allocation and deallocation at the cost of some wasted space. If you scan through the source, you’ll likely run across a variety of Buffer{T} instances; all of those come from the BufferPool.

In practice, manual memory management is not much of a problem. Pulling from the pools is actually pretty rare because almost all allocations serve big batches of work, not per-instance function calls.

The use of per-thread buffer pools for ephemeral allocations could be simplified by the use of region allocators. Rather than needing to dispose of individual allocations, the whole block can be tossed out at once when the work block is complete.

Stack allocation using the stackalloc keyword can also be used for temporary allocations with reasonable sizes. Using the stack for huge allocations isn’t a great idea, but there are plenty of cases where it fits. Just be careful about localsinit: while the C# spec doesn’t require that the stackalloc memory be zeroed, it almost always will be unless you strip the locals initialization flag with a post build step or suppress it with an upcoming attribute. This isn’t a correctness problem, but zeroing kilobytes of memory on every function call can be a noticeable performance cost.

Any blog post about memory management in C# would be incomplete without mentioning Span{T} and friends. It provides a clean abstraction over any kind of memory.

You might notice that BEPUphysics doesn’t use Span{T} at all. That’s partially because v2 started development before Span{T} was ready, and partially because almost all the memory is known to be unmanaged anyway. If you’re a user of the library, you might still find spans to be very helpful when interoperating with systems that weren’t built on the assumption of unmanaged memory.

Notably, v2 still has some reference types floating around, but they are quite rare and their number does not scale with the size of a simulation. They’re things like the Simulation object itself or its stages. There are also arrays of TypeProcessors that use abstract classes to provide indirection for batch processing, but those could just be raw function pointers if they existed. (And they might later!)

Summary

You, too, can experience the joy of manual memory management. Immerse yourself in memory leaks and access violations. Come, don’t be afraid; build your own custom allocators, because you can.

Or don’t do that, and stick to something like Span{T} instead. But at least consider giving your poor GC a break and try using nice big buffers of value types instead of a billion object instances. Mix it together with a heaping scoop of batch processing and you’ve got the recipe for efficiency with a surprising amount of simplicity, even if it looks a little bit like code from 1995 sometimes.

The leftmost type, VFloat, is the simplest representation for three scalars. It’s not a particularly fair comparison since the Vector{T} type contains 4 scalars on the tested 3770K and the Vector256{float} contains 8, so they’re conceptually doing way more work. Despite that, comparing them will reveal some interesting compiler and processor properties.

The three Add implementations tested will be a manually inlined version, a static function with in/out parameters, and an operator. Here’s how the function and operator look for VFloat; I’ll omit the manually inlined implementation and other types for brevity (but you can see them on github):

Each addition will be called several times in a loop. Some adds are independent, some are dependent. The result of each iteration gets stored into an accumulator to keep the loop from being optimized into nonexistence. Something like this:

Historically, using operators for value types implied a great deal of copying for both the parameters and returned value even when the function was inlined. (Allowing ‘in’ on operator parameters helps with this a little bit, at least in cases where the JIT isn’t able to eliminate the copies without assistance.)

To compensate, many C# libraries with any degree of performance sensitivity like XNA and its progeny offered ref/out overloads. That helped, but not being able to use operators efficiently always hurt readability. Having refs spewed all over your code wasn’t too great either, but in parameters (which require no call site decoration) have saved us from that in most cases.

But for maximum performance, you had to bite the bullet and manually inline. It was a recipe for an unmaintainable mess, but it was marginally faster!

Focusing on VFloat for a moment, how does that situation look today? Testing on the .NET Core 3.0.0-preview1-26829-01 alpha runtime:

The manually inlined version and the operator version differ by a single instruction. That’s good news- using operators is, at least in some cases, totally fine now! Also, note that there are only 12 vaddss instructions, cutting out the other 12 redundant adds. Some cleverness!

Now let’s see how things look across all the test cases…

Oh, dear. The preview nature of this runtime has suddenly become relevant. Using an operator for the VAvx type is catastrophic. Comparing the manually inlined version to the operator version:

The manually inlined variant does pretty well, producing a tight sequence of 24 vaddps instructions operating on ymm registers. Without optimizing away the redundant adds, that’s about as good as you’re going to get.

The operator version is… less good. Clearing a bunch of memory, unnecessary loads and stores, capped off with a curious function call. Not surprising that it’s 50 times slower.

Clearly something wonky is going on there, but let’s move on for now. Zooming in a bit so we can see the other results:

Both Vector{T} and AVX are slower than VFloat when manually inlined, but that’s expected given that half the adds got optimized away. Unfortunately, it looks like even non-operator functions take a hit relative to the manually inlined implementation.

When manually inlined, 8-wide AVX is also a little faster than 4-wide Vector{T}. On a 3770K, the relevant 4 wide and 8 wide instructions have the same throughput, so being pretty close is expected. The marginal slowdown arises from the Vector{T} implementation using extra vmovupd instructions to load input values. Manually caching the values in a local variable actually helps some.

Focusing on the function and operator slowdown, here’s the assembly generated for the Vector{T} function and operator cases:

Nothing crazy happening, but there’s clearly a lot of register juggling that the earlier manually inlined AVX version didn’t do. The add function versus manual inlining difference is more pronounced in the AVX case, but the cause is similar (with some more lea instructions).

But this is an early preview version. What happens if we update to a daily build from a few weeks after the one tested above?

A little better on function AVX, and more than 17 times faster on operator AVX. Not ideal, perhaps, but much closer to reasonable.

(If you’re wondering why the AVX path seems to handle things differently than the Vector{T} paths, Vector{T} came first and has its own set of JIT intrinsic implementations. The two may become unified in the future, on top of some additional work to avoid quality regressions.)

Microbenchmarks are one thing; how do these kinds of concerns show up in actual use? As an example, consider the box-box contact test. To avoid a book-length post, I’ll omit the generated assembly.

Given that manual inlining isn’t exactly a viable option in most cases, v2 usually uses static functions with in/out parameters. As expected, the generated code looks similar to the microbenchmark with the same kind of function usage. Here’s a VTune snapshot of the results:

The CPI isn’t horrible, but most of the bottleneck is related to the loading instructions. The above breaks out the 37.4% of cycles which are stalled on front-end bottlenecks. The instruction cache misses and delivery inefficiencies become relevant when there are no memory bottlenecks to hide them. With deeper analysis, many moves and loads/stores could be eliminated and this could get a nice boost.

Another fun note, from the header of BoxPairTester.Test when inlining the function is disabled:

movecx,2AAhxoreax,eaxrepstosdwordptr [rdi]

CoreCLR aggressively clears locally allocated variables if the IL locals init flag is set. Given that the flag is almost always set, it’s possible to spend a lot of time pointlessly zeroing memory. Here, the rep stos instruction performs 2AAh = 682 iterations. Each iteration sets 4 bytes of memory to the value of just-zeroed eax register, so this zeroes out 2728 bytes of stack space every single time the function is called.

In practice, many such clears are amortized over multiple function calls by forcing inlining, but unless the locals init flag is stripped, they’ll still happen. When compiled under ReleaseStrip configuration, v2 uses a post-build step to strip the locals init flag (and in the future there will likely be other options). Some simulations can improve by over 10% with the clearing stripped.

Summary

If you’re writing the kind of code where the generated assembly quality actually matters and isn’t bottlenecked by something else like memory, you should probably sanity test the performance occasionally or peek at the generated assembly to check things out. The JIT is improving, but there are limits to how much deep analysis can be performed on the fly without interfering with user experience.

And if you’re trying to use preview features that are still under active development, well, you probably know what you’re getting into.

It would be a little silly to write a series on performance in C# without mentioning the Just-In-Time (JIT) compiler. Unlike an offline toolchain that precompiles assemblies for specific platforms ahead of time (AOT), many C# applications compile on demand on the end user's device. While this does theoretically give a JIT more knowledge about the target system, it also constrains how much time is available to compile. Most users won't tolerate a 45 second startup time even if it does make everything run 30% faster afterwards.

It's worth mentioning that there are AOT compilation paths, and some platforms require AOT. Mono has historically provided such a path, .NET Native is used for UWP apps, and the newer CoreRT is moving along steadily. AOT does not always imply deep offline optimization, but the relaxation of time constraints at least theoretically helps. There's also ongoing work on tiered compilation which could eventually lead to higher optimization tiers.

One common concern is that running through any of today's JIT-quality compilers will result in inferior optimizations that render C# a dead end for truly high performance code. It's definitely true that the JIT is not able to optimize as deeply as an offline process, and this can show up in a variety of use cases.

But before diving into that, I would like to point out some important context. Consider the following simulation, a modified version of the ClothLatticeDemo. It's 65536 spheres connected by 260610 ball socket joints plus any collision related constraints that occur on impact with the table-ball-thing.

On my 3770K, it runs at about 30 ms per frame prior to impact, and about 45 ms per frame after impact. The vast majority of that time is spent in the solver executing code that looks like this (from BallSocket.cs):

It's a whole bunch of math in pretty tight loops. Exactly the kind of situation where you might expect a better optimizer to provide significant wins. And without spoiling much, I can tell you that the JIT could do better with the generated assembly here.

Now imagine someone at Microsoft (or maybe even you, it's open source after all!) receives supernatural knowledge in a fever dream and trades their soul to empower RyuJIT. Perversely blessed by the unfathomable darkness below, RyuJIT v666 somehow makes your CPU execute all instructions in 0 cycles. Instructions acting only upon registers are completely free with infinite throughput, and the only remaining cost is waiting on data from cache and memory.

How much faster would this simulation run on my 3770K when compiled with RyuJIT v666?

Take a moment and make a guess.

Infinitely faster can be ruled out- even L1 cache wouldn't be able to keep with with this demonically empowered CPU. But maybe the cost would drop from 45 milliseconds to 1 millisecond? Maybe 5 milliseconds?

From VTune:

The maximum effective bandwidth of the 3770K in the measured system is about 23 GBps. Prior to impact, the simulation is consuming 18-19 GBps of that. Post-impact, it hovers around 15 GBps, somewhat reduced thanks to the time spent in the less bandwidth heavy collision detection phase. (There's also a bandwidth usage dip hidden by that popup box that corresponds to increased bookkeeping when all the collisions are being created, but it levels back out to around 15 GBps pretty quickly.)

If we assume that the only bottleneck is memory bandwidth, the speedup is at most about 1.25x before impact, and 1.55x after. In other words, the frame times would drop to no better than 24-30 ms. Realistically, stalls caused by memory latency would prevent those ideal speedups from being achieved.

RyuJIT v666, and by extension all earthly optimizing compilers, can't speed this simulation up much. Even if I rewrote it all in C it would be unwise to expect more than a few percent. Further, given that compute improves faster than memory in most cases, newer processors will tend to benefit even less from demons.

Of course, not every simulation is quite so memory bandwidth bound. Simulations that involve complex colliders like meshes will tend to have more room for magic compilers to work. It just won't ever be that impressive.

So, could the JIT-generated assembly be better? Absolutely, and it is getting better, rapidly. Could there sometimes be serious issues for very specific kinds of code, particularly when unbound by memory bottlenecks? Yes.

But is it good enough to create many complex speedy applications? Yup!

Idiomatic C# tends to be object oriented and reliant on the garbage collector. That style can make modelling certain kinds of application logic easier, but as soon as performance is a requirement, strict OOP is a path to suffering. As this is a property of the underlying memory access patterns and the limitations of hardware, this applies to deeply OOP implementations in any language- even C++.

I won't go into a full digression on data oriented design, but the short version is that memory is extremely slow compared to everything else that a CPU is doing. In the time it takes to retrieve a piece of data from main memory, a single CPU core can execute hundreds of instructions. That stall can happen every single time a new piece of memory is touched. Large idiomatic object oriented applications tend to form an enormous web of references which, when followed, require incoherent and often uncached memory accesses. It doesn't matter how good your compiler is, that's going to leave the CPU mostly idle.

Here's an example from BEPUphysics v1, a demo of a bunch of boxes connected together to behave like stiff cloth:

Before we get into how bad this is, let me advocate for v1 a little bit here:

I disabled multithreading for this test, so all stalls remain unfilled by other thread work.

This simulation is pretty large (at least for v1) at 14400 dynamic entities and 84734 constraints, so not everything will fit into the CPU caches even under ideal circumstances.

I intentionally evicted everything from caches between updates. That might happen in some applications that need to do a bunch of non-physics work, but not all of them.

If you're not familiar with Intel VTune (which has a free license now!), 'CPI' refers to cycles per instruction and 'DRAM Bound' refers to the percentage of cycles which are stuck waiting on main memory requests. Breaking out DRAM Bound shows memory bandwidth versus memory latency bottlenecks.

Okay, how bad is this?

3-5 CPI means the CPU is doing very, very little besides waiting. Note that an ideal CPI is not 1; modern processors can execute more than one instruction per cycle in most relevant cases (see reciprocal throughput numbers on Agner Fog's always-useful instruction tables).

While not shown above, it's worth mentioning that BEPUphysics v1 was built pre-vectorization support in the runtime, so in addition to executing <<1 instruction per cycle, those instructions work with at most one scalar value at a time. This particular simulation uses about 0.4% of the 3770K's floating point throughput. To be fair, reaching even 10% can be tricky in highly optimized complex workloads, but 0.4% is just... not good.

A full 82% of cycles in the core solving function are stuck waiting on memory requests that the prefetcher could not predict, and which were not already in any cache. These requests take the form of body velocity, inertia, and constraint data, and in v1, all of them involve randomized memory accesses.

UpdateSolverActivity is mostly bottlenecked by total bandwidth rather than latency. It can be tempting to look at a bandwidth bottleneck and shrug helplessly- after all, you need a certain amount of data to compute what you need, there's not much left to do, right? But the memory subsystem of a CPU doesn't work with 4 or 12 bytes in isolation, it works with cache lines which span 64 bytes (on AMD/Intel/many other CPUs). If you ask for a boolean flag all by itself, the CPU will also pull down the surrounding cache line. If the data you truly want is sparsely distributed, the cache line utilization will be terrible and bandwidth required to serve all memory requests will be vastly higher than a tight packing.

Note that the solver executes multiple iterations each frame. The second iteration and beyond will benefit from the first iteration pulling in a bunch of the required memory into L3 cache. In this case, the simulation is too big to fit all at once, but it does hold a decent chunk. If the simulation was larger or the cache got otherwise evicted between iterations, the above numbers would be even worse.

The core issue behind all of these is pulling memory from a bunch of unpredictable places. This arises mostly from v1's OOP-y design. For example, the solver stores an array of all constraints:

An array of reference types is actually an array of pointers. Outside of rare ideal cases, it's unlikely that the data pointed to by these references will be in order and contiguous. No matter how this array is traversed, the accesses will still jump all over memory and suffer tons of cache misses.

(Side note: the v1 solver also intentionally randomizes solver order for the sake of avoiding some pathological constraint ordering which causes even more cache misses. OOPiness can't be blamed for that, but it's still something that v2 stopped doing.)

There's a lot of other subtle stuff going on here too, but the single largest takeway is that we need a way to express memory in a contiguous, structured way. Fortunately, this feature has existed since the primordial era of C#: value types.

With value types, you can create a contiguous block of memory with values lined up in order. Take the following code as an example:

Walking sequentially through the array, we can directly observe the byte values that make up the Snoot instances. There are no indirections that need to be tracked down.

So, if a traditional reference-heavy object oriented memory model isn't great, what is an alternative? BEPUphysics v2 shows one possibility. Using the solver as an example again, here is how v2 represents a group of constraints:

An important note here is that the properties of a constraint- body references, prestep data, and so on- are split into different arrays. Not every stage of constraint processing needs to access every constraint property. For example, solve iterations do not need PrestepData at all; trying to bundle prestep data with the rest of the properties would just waste valuable space in cache lines during the solve. That helps with memory bandwidth.

There's no processing logic in the TypeBatch at all, though. It's raw data. Untyped, even- the RawBuffer just refers to a block of bytes. How is it used?

Each TypeBatch contains only one type of constraint, as the name may imply. TypeBatches are... processed... by a TypeProcessor. TypeProcessors are built to understand a single kind of constraint and have no state of their own; they could easily be implemented as function pointers of static functions. In the solver, you'll find:

publicTypeProcessor[] TypeProcessors;

When the solver encounters a TypeBatch with a TypeId of 3, it can just do a quick array lookup to find the TypeProcessor which knows what to do with that TypeBatch's data.

(Side note: the cost of each virtual call is amortized over an entire type batch. Executing a type batch will tend to take hundreds of nanoseconds at an absolute minimum, so adding 2-4 nanoseconds for an indirection is pretty much irrelevant. Compare this to the OOPy approach of having a bunch of SolverUpdateable references in an array and doing a virtual call on every individual instance. Not a big deal compared to data cache misses, but still pointless overhead that can be avoided.)

In the end, this enables blasting through contiguous arrays of data with tight loops of code that look like this:

It's pure math with no branches and no constraint cache misses. While GPUs get the spotlight when it comes to compute throughput, CPUs are still extremely good at this, especially with instruction level parallelism. Every single operation in the above is executed on several constraints at the same time using SIMD instructions (which I'll cover in more detail later).

There are some cache misses hidden in that chunk of code, but they are fundamentally required by the nature of the solver algorithm- body velocities can't be stored alongside constraint data because there is no one to one mapping between them, so they must be incoherently gathered and scattered. (Side note: these cache misses can be partially mitigated; the library tries to group constraints by access patterns.)

How much of a difference does this make? Here's some more vtune data, this time of a comparable v2 simulation. Same processor, multithreading still disabled, cache still evicted between every frame.

0.454 cycles per instruction is a wee bit better than the 5+ we observed in v1. Main memory latency is pretty much eliminated as a problem. And on top of that, just about every one of those instructions is operating on multiple lanes of data with SIMD. This is a big part of why v2 is often an order of magnitude faster than v1.

Summary

Value types are not new to C# and certainly not new to programming in general, but controlling memory access patterns is absolutely critical for performance. Without this, all other attempts at optimization would be practically irrelevant.

The next step will be to see how painless we can make this style of programming in modern C#.

In the last couple of decades, managed languages have come to dominate a huge chunk of application development with a solid mix of high productivity and good-enough performance. Nowadays, you need a pretty strong justification to jump to a 'lower level' language, and that justification almost always has something to do with performance.

High frequency trading firms wouldn't want a garbage collection invocation stalling a trade by several milliseconds. Game developers working on the bleeding edge wouldn't want to sacrifice a feature or FPS target to compensate for a just-in-time compiler's lack of optimizations. It makes sense for those sorts of projects to work with a mature performance-focused ecosystem like the one around C or C++.

And then there's BEPUphysics v2- a super speedy physics simulation library built to take advantage of modern CPUs and routinely outperforms its predecessor by a factor of 10 or more, often pushing against fundamental limitations in current architectures.

And it's written from the ground up in C#, a managed language with a garbage collector and just-in-time compilation.

This is odd.

The goal of this series is to answer how this came to be, what parts of the language and runtime have enabled it, and how the future might look. Everything here should be considered a snapshot- readers a year from now should assume many things have changed. (And I might be unaware of some relevant work in the runtime right now- it's moving quickly.)

This will not be a language comparison or physics library comparsion (well, apart from bepuphysics v1 and v2). The goal is to explain a pleasantly surprising rapid evolution of complementary technologies through the lens of bepuphysics v2's development.

Historically, performance improvements in BEPUphysics v1 came incrementally. Each new big version came with a 20% boost here, another 10% over there, gradually accumulating to make v1.5.1 pretty speedy.

The jump from v1.5.1 to v2.0.0, even in its alpha state, is not incremental.

That's a change from about 120 ms per frame in v1 to around 9.5 ms in v2 when using 8 threads. The difference is large enough that the graph gets a little difficult to interpret, so the remaining graphs in this post will just show the speedup factor from v1 to v2.

I bugged a bunch of people with different processors to run the benchmarks. Some of these benchmarks were recorded with minor background tasks, so it's a fairly real world sample. All benchmarks ran on Windows 10 with .NET Core 2.0.6.

Results

First, two processors that use 128 bit SIMD:

ShapePile and Pyramids both require quite a bit of narrow phase work which involves a chunk of scalar bookkeeping. With fewer opportunities for vectorization, they don't benefit as much as the extremely solver heavy ClothLattice benchmark.

Note that the ClothLattice benchmark tends to allow v1 to catch up a little bit at higher core counts. This is largely because of limited memory bandwidth. The solver is completely vectorized apart from the bare minimum of gathers and scatters, so it's difficult to fetch constraints from memory as quickly as the cores can complete the ALU work. v1, in contrast, was just a giant mess of cache misses, and it's very easy for cores to idle in parallel.

LotsOfStatics is another interesting case: statics and inactive bodies are placed into an extremely low cost state compared to v1. In fact, the only stage that is aware of those objects at all is the broad phase, and the broad phase has a dedicated structure for inactive bodies and statics. In terms of absolute performance, the LotsOfStatics benchmark took under 350 microseconds per frame with 8 threads on the 3770K.

Notably, this is an area where v2 still has room for improvement. The broad phase is far too aggressive about refining the static structure; I just haven't gotten around to improving the way it schedules refinements yet. This particular benchmark could improve by another factor of 2-4 easily.

Now for a few AVX2-capable processors:

There are some pretty interesting things going on here. Most noticeably, the 7700HQ shows a much larger improvement across the board than any of the other processors tested. This is expected when comparing against the 128-wide platforms (3770K and 920) thanks to the significantly higher floating point throughput. The ClothLattice demo in particular shows a speedup of 10-15x compared to the 3770K's 7.4-11.9x.

While I haven't verified this with direct measurement, I suspect the 7700HQ benefits more than the AVX2 capable 4790K and 1700X thanks to its significantly higher effective memory bandwidth per core. The 4790K roughly matches the non-AVX2 3770K in bandwidth, and 1700X actually has significantly less bandwidth per core than the 3770K. The memory bandwidth hungry ClothLattice benchmark timings are consistent with this explanation- both the 4790K and 1700X show noticeably less benefit with higher core counts compared to the 7700HQ.

Zen's AVX2 implementation also has slightly different performance characteristics. I have not done enough testing to know how much this matters. If RyuJIT is generating a bunch of FMA instructions that I've missed, that could contribute to the 7700HQ's larger improvement.

There also a peculiar massive improvement in the LotsOfStatics demo, topping out at 42.9x faster on the 7700HQ. That's not something I expected to see- that benchmark spends most of its time in weakly vectorized broadphase refinements. I haven't yet done a deep dive on refinement performance (it's slated to be reworked anyway), but this could be another area where the 7700HQ's greater bandwidth is helpful.

Summary

v2 is vastly faster than v1. It's not just a way to save money on server hardware or to allow for a few more bits of cosmetic debris- it's so much faster that it can be used to make something qualitatively different.

(Certain observers may note that BEPUphysics v2 is, in fact, not yet out, nor is it even being actively developed yet. Don't worry, it's not dead or anything, I just have to get this other semirelated project out of the way. I'm hoping to get properly started on v2 in very early 2017, but do remember how good I am at estimating timelines.)

The prototype for the first big piece of BEPUphysics v2.0.0 is pretty much done: a tree.

This tree will (eventually) replace all the existing trees in BEPUphysics and act as the foundation of the new broad phase.

So how does the current prototype compare with v1.4.0's broad phase?

It's a lot faster.

The measured 'realistic' test scene includes 65536 randomly positioned cubic leaves ranging from 1 to 100 units across, with leaf size given by 1 + 99 * X^10, where X is a uniform random value from 0 to 1. In other words, there are lots of smaller objects and a few big objects, and the average size is 10 units. All leaves are moving in random directions with speeds given by 10 * X^10, where X is a uniform random value from 0 to 1, and they bounce off the predefined world bounds (a large cube) so that they stay in the same volume. The number of overlaps ranges between 65600 and 66300.

Both simulations are multithreaded with 8 threads on a 3770K@4.5ghz. Notably, the benchmarking environment was not totally clean. The small spikes visible in the new implementation do not persist between runs and are just the other programs occasionally interfering.

So, the first obvious thing you might notice is that the old version spikes like crazy. Those spikes were a driving force behind this whole rewrite. What's causing them, and how bad can they get?

The answers are refinement and really bad. Each one of those spikes represents a reconstruction of part of the tree which has expanded beyond its optimal size. Those reconstructions aren't cheap, and more importantly, they are unbounded. If a reconstruction starts near the root, it may force a reconstruction of large fractions of the tree. If you're really unlucky, it will be so close to the root that the main thread has to do it. In the worst case, the root itself might get reconstructed- see that spike on frame 0? The graph is actually cut off; it took 108ms. While a full root reconstruction usually only happens on the first frame, the other reconstructions are clearly bad enough. These are multi-frame spikes that a user can definitely notice if they're paying attention. Imagine how that would feel in VR.

To be fair to the old broad phase, this test is a lot more painful than most simulations. The continuous divergent motion nearly maximizes the amount of reconstruction required.

But there's something else going on, and it might be even worse. Notice that slow upward slope in the first graph? The new version doesn't have it at all, so it's not a property of the scene itself. What does the tree quality look like?

This graph represents the computed cost of the tree. If you've heard of surface area heuristic tree builders in raytracing, this is basically the same thing except the minimized metric is volume instead of surface area. (Volume queries and self collision tests have probability of overlap proportional to volume, ray-AABB intersection probability is proportional to surface area. They usually produce pretty similar trees, though.)

The new tree starts with poor quality since the tree was built using incremental insertion, but the new refinement process quickly reduces cost. It gets to around 37.2, compared to a full sweep rebuild of around 31.9.

The old tree starts out better since the first frame's root reconstruction does a full median split build. But what happens afterward? That doesn't look good. What happens if tree churns faster? How about a bunch of objects moving 10-100 instead of 0-10 units per second, with the same distribution?

Uh oh. The cost increases pretty quickly, and the self test cost rises in step. By the end, the new version is well over 10 times as fast. As you might expect, faster leaf speeds are even worse. I neglected to fully benchmark that since a cost metric 10000 times higher than it should be slows things down a little.

What's happening?

The old tree reconstructs nodes when their volume goes above a certain threshold. After the reconstruction, a new threshold is computed based on the result of the reconstruction. Unfortunately, that new threshold lets the tree degrade further next time around. Eventually, the threshold ratchets high enough that very few meaningful refinements occur. Note in the graph that the big refinement time spikes are mostly gone after frame 1000. If enough objects are moving chaotically for long periods of time, this problem could show up in a real game.

This poses a particularly large problem for long-running simulations like those on a persistent game server. The good news is that the new version has no such problem, the bad news is that there is no good workaround for the old version. For now, if you run into this problem, try periodically calling DynamicHierarchy.ForceRebuild (or look for the internal ForceRevalidation in older versions). As the name implies, it will reset the tree quality but at a hefty price. Expect to drop multiple frames.

(This failure is blindingly obvious in hindsight, and I don't know how I missed it when designing it, benchmarking it, or using it. I'm also surprised no one's reported it to my knowledge. Oops!)

So, how about if nothing is moving?

The old version manages to maintain a constant slope, though it still has some nasty spikes. Interestingly, those aren't primarily from refinement, as we'll see in a moment.

This is also a less favorable comparison for the new tree, "only" being 3 times as fast.

Splitting the time contributions helps explain both observations:

The old version's spikes can't be reconstructions given that everything is totally stationary, and the self test shows them too. I didn't bother fully investigating this, but one possible source is poor load balancing. It uses a fairly blind work collector, making it very easy to end up with one thread overworked. The new version, in contrast, is smarter about selecting subtasks of similar size and also collects more of them.

So why is the new refinement only a little bit faster if the self test is 3.5 times faster? Two reasons. First, the new refinement is never satisfied with doing no work, so in this kind of situation it does a bit too much. Second, I just haven't spent much time optimizing the refinement blocks for low work situations like this. These blocks are fairly large compared to the needs of a totally stationary tree, so very few of them need to be dispatched. In this case, there were only 2. The other threads sit idle during that particular subphase. In other words, the new tree is currently tuned for harder workloads.

Now, keeping leaves stationary, what happens when the density of leaves is varied? First, a sparse distribution with 8 times the volume (and so about one eighth the overlaps):

A bit over twice as fast. A little disappointing, but this is another one of those 'easy' cases where the new refinement implementation doesn't really adapt to very small workloads, providing marginal speedups.

How about the reverse? 64 times more dense than the above, with almost 500000 overlaps. With about 8 overlaps per leaf, this is roughly the density of a loose pile.

Despite the fact that the refinement suffers from the same 'easy simulation' issue, the massive improvement in test times brings the total speedup to over 5 times faster. The new tree's refinement takes less than a millisecond on both the sparse and dense cases, but the dense case stresses the self test vastly more. And the old tree is nowhere near as fast at collision tests.

Next up: while maintaining the same medium density of leaves (about one overlap per leaf), vary the number. Leaves are moving at the usual 0-10 speed again for these tests. First, a mere 16384 leaves instead of 65536:

Only about 2.5 times faster near the end. The split timings are interesting, though:

The self test marches along at around 3.5 times as fast near the end, but the refinement is actually slower... if you ignore the enormous spikes of the old version. Once again, there's just not enough work to do and the work chunks are too big at the moment. 400 microseconds pretty okay, though.

How about a very high leaf count, say, 262144 leaves?

Around 4 times as fast. Refinement has enough to chomp on.

Refinement alone hangs around 2.5-2.75 times as fast, which is pretty fancy considering how much more work it's doing. As usual, the self test is super speedy, only occasionally dropping below 4.20 times as fast.

How about multithreaded scaling? I haven't investigated higher core counts yet, but here are the new tree's results for single threaded versus full threads on the 3770K under the original 65536 'realistic' case:

Very close to exactly 4 times as fast total. Self tests float around 4.5 times faster. As described earlier, this kind of 'easy' simulation results in a fairly low scaling in refinement- only about 2.3 times faster. If everything was flying around at higher speeds, refinement would be stressed more and more work would be available.

For completeness, here's the new tree versus the old tree, singlethreaded, in the same simulation:

3 times faster refines (ignoring spikes), and about 4.5 faster in general.

How does it work?

The biggest conceptual change is the new refinement phase. It has three subphases:

1) Refit

As objects move, the node bounds must adapt. Rather than doing a full tree reconstruction every frame, the node bounds are recursively updated to contain all their children.

During the refit traversal, two additional pieces of information are collected. First, nodes with a child leaf count below a given threshold are added to 'refinement candidates' set. These candidates are the roots of a bunch of parallel subtrees. Second, the change in volume of every node is computed. The sum of every node's change in volume divided by the root's volume provides the change in the cost metric of the tree for this frame.

2) Binned Refine

A subset of the refinement candidates collected by the refit traversal are selected. The number of selected candidates is based on the refit's computed change in cost; a bigger increase means more refinements. The frame index is used to select different refinement candidates as time progresses, guaranteeing that the whole tree eventually gets touched.

The root always gets added as a refinement target. However, the refinement is bounded. All of these refinements tend to be pretty small. Currently, any individual refinement in a tree with 65536 leaves will collect no more than 768 subtrees, a little over 1%. That's why there are no spikes in performance.

Here's an example of candidates and targets in a tree with 24 leaves:

The number within each node is the number of leaves in the children of that node. Green circles are leaf nodes, purple circles are refinement candidates that weren't picked, and red circles are the selected refinement targets. In this case, the maximum number of subtrees for any refinement was chosen as 8.

Since the root has so many potential nodes available, it has options about which nodes to refine. Rather than just diving down the tree a fixed depth, it seeks out the largest nodes by volume. Typically, large nodes tend to be a high leverage place to spend refine time. Consider a leaf node that's moved far enough from its original position that it should be in a far part of the tree. Its parents will tend to have very large bounds, and refinement will see that.

The actual process applied to each refinement target is just a straightforward binned builder that operates on the collected nodes. (For more about binned builders, look up "On fast Construction of SAH-based Bounding Volume Hierarchies" by Ingo Wald.)

3) Cache Optimize

The old tree allocated nodes as reference types and left them scattered through memory. Traversing the tree was essentially a series of guaranteed cache misses. This is not ideal.

The new tree is just a single contiguous array. While adding/removing elements and binned refinements can scramble the memory order relative to tree traversal order, it's possible to cheaply walk through parts of the tree and shuffle nodes around so that they're in the correct relative positions. A good result only requires optimizing a fraction of the tree; 3% to 5% works quite well when things aren't moving crazy fast. The fraction of cache optimized nodes scales with refit-computed cost change as well, so it compensates for the extra scrambling effects of refinement. In most cases, the tree will sit at 80-95% of cache optimal. (Trees with only a few nodes, say less than 4096, will tend to have a harder time keeping up right now, but they take microseconds anyway.)

Cache optimization can double performance all by itself, so it's one of the most important improvements.

As for the self test phase that comes after refinement, it's pretty much identical to the old version in concept. It's just made vastly faster by a superior node memory layout, cache friendliness, greater attention to tiny bits, and no virtual calls.

Interestingly, SIMD isn't a huge part of the speedup. It's used here and there (mainly refit), but not to its full potential. The self test in particular, despite being the dominant cost, doesn't use SIMD at all.

Future work

1) Solving the refinement scaling issue for 'easy' simulations would be nice.

2) SIMD is a big potential area for improvement. As mentioned, this tree is mostly scalar in nature. At best, refit gets decent use of 3-wide operations. My attempts at creating fully vectorized variants tended to do significantly better than the old one, but they incurred too much overhead in many phases and couldn't beat the mostly scalar new version. I'll probably fiddle with it some more when a few more SIMD instructions are exposed, like shuffles; it should be possible to get at least another 1.5 to 2 times speedup.

3) Refinement currently does some unnecessary work on all the non-root treelets. They actually use the same sort of priority queue selection, even though they are guaranteed to eat the whole subtree by the refinement candidate collection threshold. Further, it should be possible to improve the node collection within refinement by taking into account the change in subtree volume on a per-node level. The root refinement would seek out high entropy parts of the tree. Some early testing implied this would help, but I removed it due to memory layout conflicts.

4) I suspect there are some other good options for the choice of refinement algorithm. I already briefly tried agglomerative and sweep refiners (which were too slow relative to their quality advantage), but I didn't get around to trying things like brute forcing small treelet optimization (something like "Fast Parallel Construction of High-Quality Bounding Volume Hierarchies"). I might revisit this when setting up the systems of the next point.

5) It should be possible to improve the cache optimization distribution. Right now, the multithreaded version is forced into a suboptimal optimization order and suffers from overhead introduced by lots of atomic operations. Some experiments with linking cache optimization to the subtrees being refined showed promise. It converged with little effort, but it couldn't handle the scrambling effect of root refinement. I think this is solvable, maybe in combination with #4.

6) Most importantly, all of the above assumes a bunch of dynamic leaves. Most simulations have tons of static or inactive objects. The benchmarks show that the new tree doesn't do a bad job on these by any means, but imagine all the leaves were static meshes. There's no point in being aggressive with refinements or cache optimizations because nothing is moving or changing, and there's no need for any collision self testing if static-static collisions don't matter.

This is important because the number of static objects can be vastly larger than the number of dynamic objects. A scene big enough to have 5000 active dynamic objects might have hundreds of thousands of static/inactive objects. The old broad phase would just choke and die completely, requiring extra work to use a StaticGroup or something (which still wouldn't provide optimal performance for statics, and does nothing for inactive dynamics). In contrast, a new broad phase that has a dedicated static/inactive tree could very likely handle it with very little overhead.

When I have mentioned big planned broad phase speedups in the past ("over 10 times on some scenes"), this is primarily what I was referring to. The 4 times speedup of the core rewrite was just gravy.

Now what?

If you're feeling adventurous, you can grab the tree inside of the new scratchpad repository on github. Beware, it's extremely messy and not really packaged in any way. There are thousands of lines of dead code and diagnostics, a few dependencies are directly referenced .dlls rather than nice nuget packages, and there's no documentation. The project also contains some of the vectorized trees (with far fewer features) and some early vectorized solver prototyping. Everything but the Trees/SingleArray tree variant is fairly useless, but it might be interesting to someone.

In the future, the scratchpad repo will be where I dump incomplete code scribblings, mostly related to BEPUphysics.

I'm switching developmental gears to some graphics stuff that will use the new tree. It will likely get cleaned up over time and turned into a more usable form over the next few months. A proper BEPUphysics v2.0.0 repository will probably get created sometime in H1 2016, though it will remain incomplete for a while after that.

A lot of exciting stuff has happened in the .NET world over the last year, and BEPUphysics is approaching some massive breaking changes. It seems like a good time to condense the plans in one spot.

First, expect v1.4.0 to get packaged up as a stable release in the next couple of months. At this time, I expect that v1.4.0 will likely be the last version designed with XNA platform compatibility in mind.

Following what seems to be every other open source project in existence, BEPUphysics will probably be moving to github after v1.4.0 is released.

Now for the fun stuff:

BEPUphysics v2.0.0

High Level Overview:

Performance drives almost everything in v2.0.0. Expect major revisions; many areas will undergo total rewrites. Applications may require significant changes to adapt. The revisions follow the spirit of the DX11/OpenGL to DX12/Vulkan shift. The engine will focus on providing the highest possible performance with a minimal API.

Expect the lowest level engine primitives like Entity to become much 'dumber', behaving more like simple opaque data blobs instead of a web of references, interfaces, and callbacks. The lowest layer will likely assume the user knows what they're doing. For example, expect a fundamental field like LinearVelocity to be exposed directly and without any automatic activation logic. "Safe" layers that limit access and provide validation may be built above this to give new users fewer ways to break everything.

Features designed for convenience will be implemented at a higher level explicitly separated from the core simulation or the responsibility will be punted to the user.

Some likely victims of this redesign include:
-Internal timestepping. There is really nothing special about internal timestepping- it's just one possible (and very simple) implementation of fixed timesteps that could, and probably should, be implemented externally.
-Space-resident state buffers and state interpolation. Users who need these things (for asynchronous updates or internal timestepping) have to opt in anyway, and there's no reason to have them baked into the engine core.
-All deferred collision events, and many immediate collision events. The important degrees of access will be retained to enable such things to be implemented externally, but the engine will do far less.
-'Prefab' entity types like Box, Sphere, and so on are redundant and only exist for legacy reasons. Related complicated inheritance hierarchies and generics to expose typed fields in collidables will also likely go away.
-'Fat' collision filtering. Some games can get by with no filtering, or just bitfields. The engine and API shouldn't be hauling around a bunch of pointless dictionaries for such use cases.And more.

Platform Support:

Expect older platforms like Xbox360 and WP7 to be abandoned. The primary target will be .NET Core. RyuJIT and the new SIMD-accelerated numeric types will be assumed. Given the new thriving open source initiative, I think this is a safe bet.

Going forward, expect the engine to adopt the latest language versions and platform updates more rapidly. The latest version of VS Community edition will be assumed. Backwards compatibility will be limited to snapshots, similar to how v1.4.0 will be a snapshot for the XNA-era platforms.

Areas of Focus:

1) Optimizing large simulations with many inactive or static objects

In v1.4.0 and before, a common recommendation is to avoid broadphase pollution. Every static object added to the Space is one more object to be dynamically handled by the broad phase. To mitigate this issue, bundling many objects into parent objects like StaticGroups is recommended. However, StaticGroups require explicit effort, lack dynamic flexibility, and are not as efficient as they could be.

Inactive objects are also a form of broadphase pollution, but unlike static objects, they cannot be bundled into StaticGroups. Further, these inactive objects pollute most of the other stages. In some cases, the Solver may end up spending vastly more time testing activity states than actually solving anything.

Often, games with these sorts of simulations end up implementing some form of entity tracking to remove objects outside of player attention for performance reasons. While it works in many cases, it would be better to not have to do it at all.

Two large changes are required to address these problems:-The BroadPhase will be aware of the properties of static and inactive objects. In the normal case, additional static or inactive objects will incur almost no overhead. (In other words, expect slightly less overhead than the StaticGroup incurs, while supporting inactive dynamic objects.)-Deactivation will be redesigned. Persistent tracking of constraint graphs will be dropped in favor of incremental analysis of the active set, substantially reducing deactivation maintenance overhead. Stages will only consider the active set, rather than enumerating over all objects and checking activity after the fact.

On the type of simulations hamstrung by the current implementation, these changes could improve performance hugely. In extreme cases, a 10x speedup without considering the other implementation improvements or SIMD should be possible.

2) Wide parallel scaling for large server-style workloads

While the engine scales reasonably well up to around 4 to 6 physical cores, there remain sequential bottlenecks and lock-prone bits of code. The NarrowPhase's tracking of obsolete collision pairs is the worst sequential offender. More speculatively, the Solver's locking may be removed in favor of a batching model if some other changes pan out.

The end goal is decent scaling on 16-64 physical cores for large simulations, though fully achieving this will likely require some time.

3) SIMD

With RyuJIT's support for SIMD types comes an opportunity for some transformative performance improvements. However, the current implementation would not benefit significantly from simply swapping out the BEPUutilities types for the new accelerated types. Similarly, future offline optimizing/autovectorizing compilers don't have much to work with under the current design. As it is, these no-effort approaches would probably end up providing an incremental improvement of 10-50% depending on the simulation.

To achieve big throughput improvements, the engine needs cleaner data flow, and that means a big redesign. The solver is the most obvious example. Expect constraints to undergo unification and a shift in data layout. The Entity object's data layout will likely be affected by these changes. The BroadPhase will also benefit, though how much is still unclear since the broad phase is headed for a ground up rewrite.

The NarrowPhase is going to be the most difficult area to adapt; there are a lot of different collision detection routines with very complicated state. There aren't as many opportunities for unification, so it's going to be a long case-by-case struggle to extract as much performance as possible. The most common few collision types will most likely receive in-depth treatment, and the remainder will be addressed as required.

Miscellaneous Changes:

-The demos application will move off of XNA, eliminating the need for a XNA Game Studio install. The drawer will be rewritten, and will get a bit more efficient. Expect the new drawer to use DX11 (feature level 11_0) through SharpDX. Alternate rendering backends for OpenGL (or hopefully Vulkan, should platform and driver support be promising at the time) may be added later for use in cross platform debugging.

-As alluded to previously, expect a new broad phase with a much smoother (and generally lower) runtime profile. Focuses on incremental refinement; final quality of tree may actually end up higher than the current 'offline' hierarchies offered by BEPUphysics.

-StaticGroup will likely disappear in favor of the BroadPhase just handling it automatically, but the non-BroadPhase hierarchies used by other types like the StaticMesh should still get upgraded to at least match the BroadPhase's quality.

-Wider use of more GC-friendly data structures like the QuickList/QuickSet to avoid garbage and heap complexity.

-Convex casts should use a proper swept test against the broad phase acceleration structure. Should make long unaligned casts much faster.

-More continuous collision detection options. Motion clamping CCD is not great for all situations- particularly systems of lots of dynamic objects, like passengers on a plane or spaceship. The existing speculative contacts implementation helps a little to stabilize things, but its powers are limited. Granting extra power to speculative contacts while limiting ghost collisions would be beneficial.

-The CompoundShape could use some better flexibility. The CompoundHelper is testament to how difficult it can be to do some things efficiently with it.

Schedule Goals:

Variable. Timetable depends heavily on what else is going on in development. Be very suspicious of all of these targets.

Expect the earliest changes to start showing up right after v1.4.0 is released. The first changes will likely be related the debug drawer rewrite.

The next chunk may be CCD/collision pair improvements and the deactivation/broadphase revamp for large simulations. The order of these things is uncertain at this time because there may turn out to be some architectural dependencies. This work will probably cover late spring to mid summer 2015.

Early attempts at parallelization improvements will probably show up next. Probably later in summer 2015.

SIMD work will likely begin at some time in late summer 2015. It may take a few months to adapt the Solver and BroadPhase.

The remaining miscellaneous changes, like gradual improvements to collision detection routines, will occur over the following months and into 2016. I believe all the big changes should be done by some time in spring 2016.

This work won't be contiguous; I'll be hopping around to other projects throughout.

Future Wishlist:

-The ancient FluidVolume, though slightly less gross than it once was, is still very gross. It would be nice to fix it once and for all. This would likely involve some generalizations to nonplanar water- most likely procedural surfaces that would be helpful in efficiently modeling waves, but maybe to simple dynamic heightfields if the jump is short enough.

-Fracture simulation. This has been on the list for a very long time, but there is still a chance it will come up. It probably won't do anything fancy like runtime carving or voronoi shattering. More likely, it will act on some future improved version of CompoundShapes, providing different kinds of simple stress simulation that respond to collisions and environmental effects to choose which parts get fractured. (This isn't a very complicated feature, and as mentioned elsewhere on the forum, I actually implemented something like it once before in a spaceship game prototype- it just wasn't quite as efficient or as clean as a proper release would require.)

On GPU Physics:

In the past, I've included various kinds of GPU acceleration on the development wishlist. However, now, I do not expect to release any GPU-accelerated rigid body physics systems in the foreseeable future. BEPUphysics itself will stay exclusively on the CPU for the foreseeable future.

I've revisited the question of GPU accelerated physics a few times over the last few years, including a few prototypes. However, GPU physics in games is still primarily in the realm of decoration. It's not impossible to use for game logic, but having all of the information directly accessible in main memory with no latency is just a lot easier.

And implementing individually complicated objects like the CharacterController would be even more painful in the coherence-demanding world of GPUs. (I would not be surprised if a GPU version of a bunch of full-featured CharacterControllers actually ran slower due to the architectural mismatch.) There might be a hybrid approach somewhere in here, but the extra complexity is not attractive.

And CPUs can give pretty-darn-decent performance. BEPUphysics is already remarkably quick for how poorly it uses the capabilities of a modern CPU.

And our own game is not a great fit for GPU simulation, so we have no strong internal reason to pursue it. Everything interacts heavily with game logic, there are no deformable objects, there are no fluids, any cloth is well within the abilities of CPU physics, and the clients' GPUs are going to be busy making pretty pictures.

This all makes implementing runtime GPU simulation a bit of a hard sell.

That said, there's a small chance that I'll end up working on other types of GPU accelerated simulation. For example, one of the GPU prototypes was a content-time tool to simulate flesh and bone in a character to automatically generate vertex-bone weights and pose-specific morph targets. We ended up going another direction in the end, but it's conceivable that other forms of tooling (like BEPUik) could end up coming out of continued development.

Have some input? Concerned about future platform support? Want to discuss the upcoming changes? Post on the forum thread this was mirrored from, or just throw tweets at me.

With this new version comes some changes to the forks. I've dropped the SlimDX and SharpDX forks in favor of focusing on the dependency free main fork.

The XNA fork will stick around for now, but the XNA fork's library will use BEPUutilities math instead of XNA math. Going forward, the fork will be used to maintain project configuration and conditional compilation requirements for XNA platforms.